_:n5b093bc754af4c7a84698a5bf60ea9b3xb11
<http://schema.org/description>
"We propose a novel autoencoding model called Pairwise Augmented GANs. We\ntrain a generator and an encoder jointly and in an adversarial manner. The\ngenerator network learns to sample realistic objects. In turn, the encoder\nnetwork at the same time is trained to map the true data distribution to the\nprior in latent space. To ensure good reconstructions, we introduce an\naugmented adversarial reconstruction loss. Here we train a discriminator to\ndistinguish two types of pairs: an object with its augmentation and the one\nwith its reconstruction. We show that such adversarial loss compares objects\nbased on the content rather than on the exact match. We experimentally\ndemonstrate that our model generates samples and reconstructions of quality\ncompetitive with state-of-the-art on datasets MNIST, CIFAR10, CelebA and\nachieves good quantitative results on CIFAR10."
<https://deepai.org/organization/hse-university>